See what your AI, cloud and data spend produces, as well as what it costs.
One engine on the open FOCUS schema covers cloud, AI, data platforms, Kubernetes, on-premise and SaaS, plus any cost domain you add later.
Used by Fortune 500 companies and delivered with global systems integrators including Infosys and Wipro.
First insight in 48 hours · Fixed annual subscription · EDP and MACC eligible
The platform is the same for both. The first thing you need to see is not.
You need the security review answered, a deployment model that keeps data inside your perimeter, and a procurement path that runs through the integrator you already work with.
You need to see it working before you talk to anyone, know it will not tax your growth, and get a unit metric in front of your board by the end of the month.
Customers
A leading bank 
Delivered and co-sold by
Available on
Azure Marketplace Cloud partner programs
Built on open standards, not proprietary lock-in
A cost number without a value denominator is an invitation to cut the wrong thing.
Without per-request attribution there is no cost per workflow, and without cost per workflow there is no return on workflow. That chain is what DigiUsher builds, in the order an enterprise actually needs it: can it run here, is the number right, whose money is it, what did the money produce, what will it cost next, where is the waste, and how do we stop it happening again.
Bring Your Own Cloud puts the entire platform, control plane included, inside your own account. No cost, usage, telemetry or workload data leaves your infrastructure, which is what lets a vendor risk team treat DigiUsher as software supply rather than an outsourcing arrangement.
How the deployment qualifies →
Signed off by the security board, enterprise architecture and procurement
Every record lands in the open FOCUS schema at ingestion and reconciles to the invoice, with on-premise and mainframe cost in the same ledger as cloud. There is no proprietary intermediate schema, so the data stays portable.
FinOps lead · finance systems
Sequenced allocation from invoice to environment to project to team, with each stage recording its rule, inputs and outputs, and attribution quality graded. Most tools attribute spend to a person, a card or a contract. An agent running overnight has none of the three.
FinOps lead · platform engineering
Cost per workload, per merged pull request, per pipeline run and per service, composing into cost per customer with AI included. Everyone claims unit economics. Almost nobody ingests the business metric that forms the denominator.
CFO · CIO and CTO · product
Forecasts against the committed position, rate and negotiation support, build versus buy modeling, and budget integrity that holds when a new workload lands mid-quarter.
CFO · FinOps lead · procurement
Scenario libraries per domain plus your own rules, each recommendation carrying severity, saving and evidence. Finding waste is the commoditized part. Executing the fix, with approval and an audit trail, is not.
Platform engineering · FinOps lead
Ownership assigned at creation, guardrails that fire before the spend, accountability routed to the team that can act, and an audit record of every change. An AI agent raises the pull request, a person approves it, Terraform applies it.
FinOps lead · platform engineering · internal audit
Because every domain lives in one schema, unit metrics compose across domains. Cost per customer can include the Databricks credits, the Bedrock tokens, the EKS pods and the SaaS seats that serve that customer, as one formula with one auditable answer. A point tool can only compute its own slice. The whole number needs the whole estate in one place.
Two disciplines already own real ground. FinOps established cloud cost accountability. Technology Business Management established IT financial planning. Both remain necessary, and neither was designed to hold cost and business output in the same schema.
Asks: is this cloud cost visible, attributed and optimized?
DigiUsher writes to FOCUS and aligns to the FinOps Foundation. TVR depends on a mature FinOps practice rather than replacing one.
Asks: how should IT budget be planned, allocated and defended?
DigiUsher feeds TBM. Attributed operational cost and unit economics flow upward into Apptio-class planning models. Enterprises run both.
Asks: what did this cost produce, who owns it, and is the value compounding faster than the spend?
The operational cost-to-value layer, applied uniformly to every domain.
How the three layers sit together
TVR builds on a FinOps practice rather than replacing it, and it does not produce next year's IT budget. That remains TBM's job.
Cost tools stop at visibility. Boards are asking what the spend produced. This is how the two approaches differ.
| Cost-reporting tools Conventional | DigiUsher Technology Value Realization | |
|---|---|---|
| Scope | One domain per tool | Cloud, AI, data platforms, Kubernetes, on-premise and SaaS on one open schema |
| What they show | Yesterday's bill, by service | Live cost by owner, by unit and by business outcome, with attribution quality graded |
| Value metrics | Stops at visibility | Cost per workload, per merged pull request, per pipeline run, per service, and cost per customer with AI included |
| Deployment | SaaS only. Your data leaves you | SaaS, a dedicated instance, or BYOC, with full feature parity and DigiUsher as a software provider rather than a data processor |
| Pricing | Percentage of spend, which penalizes growth | Fixed annual subscription, eligible for EDP drawdown and MACC |
| Action | Dashboards, alerts and spreadsheets | Recommendation to pull request to approval to applied, MCP enabled |
| Automation | Built-in, fixed-scope automation modules | Composes your existing AI and RPA stack into governed workflow automation, with change management built in |
| Savings | Estimated, then forgotten | Tracked from identified, to applied, to verified in the bill. The number finance will sign off |
Cloud, data and AI return in one number, with chargeback finance can defend.
Unit economics per service, and guardrails that fire before the bill arrives.
Auditable sequenced allocation, executive-ready reports, in real time rather than at month end.
Each domain gets its own connectors, waste patterns and signature value metric, on the same open schema, so the metrics compose.
All five editions in depth, with connectors, waste scenarios and value metrics →
Cloud, data platforms, AI models, coding agents, Kubernetes and SaaS land in FOCUS at ingestion, so a token, a DBU and a cloud instance are directly comparable. A new source is a connector, not a platform release.
Cloud
Data platforms
AI models
Coding agents
Kubernetes
SaaS & on-premise
See how each domain's connectors, waste patterns and value metric work →
Pick the model that fits your regulatory posture. Prompts, responses and tool payloads are discarded at ingestion, whatever the connector configuration, so privacy is built into the pipeline rather than promised in a contract.
Hosted by DigiUsher. Fastest time to value.
Startups, scale-ups and growth-stage cloud-native teams.
Single tenant in a region you pick. Your isolation, our operations.
Mid-market enterprise, EU data residency, regulated SaaS.
DigiUsher runs entirely inside your AWS, Azure, GCP, OCI or data center account. No cost, usage, telemetry or workload data leaves your infrastructure.
Banking, insurance, public sector.
Your cloud account
VPC · cost data · usage logs · billing exports · k8s metrics
Control plane
Metadata only · dashboards · forecasts · alerts
Under BYOC the whole platform, control plane included, runs inside your perimeter. Individual-level views are RBAC-gated, with an organization-wide aggregate-only mode for works-council and compliance postures. There is an audit record of every connector, credential and visibility change.
Data residency rules ruled out percentage-of-spend SaaS tools before any evaluation started.
System-table-level ingestion put cloud, warehouse and pipeline cost in one ledger, and allocated spend that had been split across three teams.
Cost per pipeline run, tracked from the first week.
€1M verified in the bill in 45 days, and two pipelines rebuilt rather than cut.
Cloud and AI spend climbing faster than revenue, with no per-product unit economics to argue from.
Real-time allocation by service, surfaced in the dashboards engineering already used.
80% less time spent on monthly allocation, and product-level cost owned by the teams that create it. Darshan Datt KS, Director of Engineering
Multi-cloud sprawl, no chargeback model, and finance and engineering disagreeing about cost drivers.
Allocation engine with shared-service chargeback, and finance-ready reports without a spreadsheet stage.
25% cloud cost reduction inside two quarters, on a chargeback model finance signed off. Vikranth Ramanola, Co-founder and CTO
Technology Value Realization (TVR) is the discipline of connecting every technology cost, across AI, cloud, data platforms, Kubernetes, on-premise and SaaS, to the business value it produces. DigiUsher organizes it as seven questions an enterprise has to answer about technology money: whether the platform can run inside your estate, whether the number is right, whose money it is, what the money produced, what it will cost next, where the waste is, and how overspend is prevented.
AWS, Azure, GCP, OCI and Alibaba Cloud billing; Kubernetes at node, pod, cluster, daemonset, replicaset, deployment and namespace level; Databricks, BigQuery and MongoDB Atlas; Anthropic, OpenAI, Cursor, Bedrock, Vertex AI and Azure AI; coding-agent telemetry from Claude Code, Codex and Cursor; on-premise, VMware and mainframe estates; and SaaS subscriptions including GitHub, Microsoft 365, Salesforce, ServiceNow and Google Workspace. Snowflake support is in development. Adding a source means adding a connector, not waiting for a release.
Every cost record lands in FOCUS, the FinOps Open Cost and Usage Specification, at the moment of ingestion. There is no proprietary intermediate schema and no translation layer. That delivers roughly 30% lower data-processing cost, no translation delay, and a dataset your team owns and can take anywhere. The cost figure is a DigiUsher internal measure, aggregated and anonymized across customer deployments.
Across three surfaces: managed AI platforms including Bedrock, Vertex AI and Azure AI; direct model providers including Anthropic, OpenAI and Gemini; and engineering agents including Cursor, Claude Code, Copilot, Codex, Windsurf, Gemini CLI and Devin. GPU infrastructure is covered down to MIG-partition level. The AI Attribution Lens joins agent spend to delivered work and reports cost per merged pull request. Prompts and responses are discarded at ingestion by architectural rule.
Three models with full feature parity: SaaS, a dedicated instance, and Bring Your Own Cloud (BYOC). Under BYOC the platform runs entirely inside your cloud or data center. No cost, usage, telemetry or workload data leaves your infrastructure, and DigiUsher is classified as a software provider rather than a data processor under FCA, PRA, MAS, DORA, FedRAMP and IL2/IL4 regimes.
Flat-rate license pricing based on an annual consumption tier, never a percentage of spend. As technology spend grows, and AI spend can grow tenfold in a year, DigiUsher's price stays flat or increases marginally according to your contractual tier. Procurement runs through our global systems integrators, directly, or via AWS Marketplace drawing down EDP commitments, or Azure Marketplace counting toward MACC.
First insight within 48 hours of connecting a source, measured across our customer base rather than at one account, and full enterprise integration in 2 to 4 weeks. Each stage delivers value on its own, so adoption does not wait on a big-bang program.
Yes. DigiUsher exposes its full data model through the Model Context Protocol (MCP). Claude, Copilot, Gemini or an in-house model can query cost, allocation and value data conversationally, inside your own AI environment, subject to the same role-based access control as every dashboard.
44 more answers, covering TVR, FinOps, AI cost, Kubernetes, allocation and vendor selection →
A 15-minute call about your stack, then first insight within 48 hours of onboarding.
